Improving inspection data quality in pipeline corrosion assessment

Abstract

The advances of computational methods and tools can greatly support other areas in doing tasks from the most tedious or repetitive to the most complex. In this paper, these advances were manipulated in civil structures maintenance specifically in pipeline corrosion assessment. This paper describes mechanize method developed to improved the quality of In-line inspection (ILI) data by automatically detect and quantify important parameters for future prediction of corrosion growth. The focal process in this system includes data conversion, data filtering, parameter tolerance or sizing configuration, matching, and data trimming. A sensitivity analysis using linear regression method was used to correlates defects from one inspection to the next. Issues and advantage gain from this mechanize system is threefold: timeliness, accuracy and consistencies in data sampling.